Karanja Evanson Mwangi - AIBUMA

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SOFTWARE DEVELOPMENT INDUSTRY IN EAST AFRICA: KNOWLEDGE
MANAGEMENT PERSPECTIVE AND VALUE PROPOSITION.


BY Karanja Evanson Mwangi, Lawrence Xavier Thuku and John Patrick Kangethe


Karanjae@gmail.com


Abstract.

Increased usage of internet has contributed imm
ensely to the growth of software
development practice in East Africa.
This paper investigates

the existence of
formal
KM (Knowledge Management)
initiatives

in the Software industry
such as
creation of virtual communities

( Communities of practice and c
ommunities of
interest); expert localization and establishment of knowledge taxonomies in these
communities; the knowledge transfer and sharing processes; incubation and
Mentorship; collaborative software development


and their

role in creating
entre
prenuership intiatives and providing a building block towards the knowledge
economies.

We propose a hybrid framework
for use

in KM intiative focusing on
Software Development in East
Africa
.


1.

Introduction

1.1.

What Constitutes Knowledge?

Researchers in the
field of knowledge management acknowledges the complexity
of isolating and defining knowledge, its constituents and their dependencies
(Bergeron, 2003)
,
(Bouthillier & Shearer, 2002)

. Due to the interdisciplinary
nature of Knowledge Management (KM), the attempts to def
ine these various
concepts and constituents slightly differ depending on the discipline of influence
and context. For the purposes of this paper, we identif
ied

the following
constituents:
Data, information, knowledge and instrumental understanding.

A

r
eview on KM literature shows that t
here exists variances and overlaps in the
definitions

of these constituent
across the
authors
.

(Meadow, Boyce, &

Kraft, 2000)

defines data as strings of elementary symbols,
such as digits and letters. As they argue that information is generally made up of
2


evaluated or useful data. Knowledge has higher degree of validity and
has

characteristics of information shar
ed and agreed by a community”. Meadow, et al.
relates instrumental understanding to intelligence which they define as a measure
of reasoning capacity.


(Wiig, 1999)

defines information as organized data and knowledge as a set truth
and belief.
(Bergeron, 2003)

argues that data are numerical quantities drawn from
observation, experiment or calculation whereas Information is applied
data:

-

“collection of data and associated explanations, interpretations, and
other textual
material concerning a particular object, event, or process”.
Bergeron introduces
metadata as a link between information and knowledge, which he defines as:
“information about the context in which information is used”. Knowledge is
illustrate
d as a mix of metadata and awareness of the context which metadata can
successfully be applied.


(Zack, 1999)

defines data as observations and fact out of a context that has no
direct meaning and information is data within a meaningful context.

(Liew, 2007)
,
(Govil, 2007)

obs
erved that data must be processed (to be put into

meaningful
context) to obtain information that a decision can be based on. Knowledge is
derived from validated information and differentiated through experience whereas
instrumental understanding is the ut
ilization of accumulated knowledge.
Therefore, there is need
for proper data management, information management
and knowledge management in their hierarchical relationship

so as to realize the
aim of KM

(Govil, 2007; Hick, 2006)
.


In
general the above definitions highlight the overlap between the constituents of
knowledge. There seems to be a consensus among
the
authors on three issues
namely; the relationship between adjacent constituents, the validity of constituents
is dependen
t on the context and the hierarchical direction (Data at the bottom of
the hierarchy and instrumental understanding at the top) as illustrated in
Figure 1
below (Knowledge continuum).




3







2.

Background Study of Knowledge Management

2.1
The Multi
-

Face
t nature
of Knowledge

Management



The practice of KM has been
there for a long time mainly in the informal
way
(Pandya & Xiaoming, 2003)
.
There
are numerous working definitions
of

KM


(Bouthillier & Shearer, 2002)

cited the work of

(Hlupic,
Pouloudi, & Rzevski, 2002)

which identified 18 definitions of K
M in
different contexts .

The working definition used by the authors of this paper
is:



Is the ability of a community to create, validate, audit, share knowledge
using appropriate technologies to gain competitive
advantage




The KM thinking and
praxis is

informed and influenced by various
disciplines
.
(Kakabadse, Kakabadse, & Kouzmin, 2003)

study infers as
follows on the nature and sources of
influence:

4



“ philosophy, in defining knowledge; cognitive science (i
n understanding
knowledge workers); social science (understanding motivation, people,
interactions, culture, environment); management science (optimizing
operations and integrating them within the enterprise); information
science (building knowledge
-
relate
d capabilities); knowledge engineering
(eliciting and codifying knowledge); artificial intelligence (automating
routine and knowledge
-
intensive work) and econ
omics (determining
priorities).”



Knowledge management is a multi
-

facet
discipline

stretching ac
ross numerous
economic
sectors.
Organizations

within

those sectors have

differing approaches
based

on

theoretical perceptions or practical
experiences

on how knowledge can be
effectively managed i.e
created,

validated,

transferred and re
-
used.


2.2 KM

an
d intellectual capital


As

o
rganization in these sectors

attempt to
move the

knowledge realms
from
cognitions

and abi
lities of individuals to a
vital
transitional

asset
,

they are faced
with the challenge
of
organizing

and
leveraging

its
intellectual

cap
ital faster than
their

competition
(Bontis, 2001)

.

Different studies o
n

inta
n
gible assets identify
three

major components of intellectual
capital

namely;
human

capital, customer
capital,
and structural

capital.

(Bergeron, 2003)
,

(José, 2003)

,

(Edvinsson &
Malone,
1997)
,
(Sullivan, 2000)
,
(Sveiby, 1997)
,
(Kaplan & Norton, 1996)
.



(Pike, Rylander, & Roos, 2002)

use
s
the

term relational capital to ref
er to
customer capital.



(VanBuren, 1999)
,
(Hsu & Mykytyn, 2006)

isolate
s the

structural capital to innovation capital and process capital
and argue
s

that there
exist an intuitive link between the various components of intellectual capital.
The
effective management of these intellectual
capital
components and their inter
-
relationship is an important step towards
organizational

learning

and m
arket
leadership.


5


Numerous

researchers have
investigated

the
KM and

Intellectual capital issues
from an
organizational

perspective.

In this paper we extend the concept of
intellectual capital

and
KM beyond

the
organization

view to
a community

view of
kn
owledge that spans among different
organizations

and individuals who practice
or have interest in software development.

Recent advances in software
development especially emergence of

active

communities (
localized

or
virtual)

have
necessitated

critical
con
sideration of

KM as

an integral

part of th
e
practice
and success

of software industry

(Hemetsberger & Reinhardt, 2003)
.



2.3
Knowledge Management
practice
in East Africa

The East Africa Region is made up of three
nations
;

Kenya,

Uganda and Tanzania.


KM has

been going on informally and intuiti
vely in the East African region.
Empirical

study

based on a Kenyan perspective

that shows of KM
initiatives

are
firm based
(Mosoti & Masheka, 2010)
.



Organizations


in the

East
Africa

region
uses

in
-
house
approaches or strategic
partnerships

as ways of
realizing


Knowledge management,
however
to measure the effectiveness of these practices
is difficult due existing
organizational

culture and vocational reinforcers that
induce the not
ion
that knowledge sharing among
organizations

in the similar or
complementary industries

may reduce their competitive advantage

and market
leverage
.

Formal KM is an emergent area with great value proposition in Africa
(Karanja, 2010)

.


Knowledge Management Africa (KMA
) is

one of
the new

initiatives that
aim

at

driving
KM
initiatives

in Africa.



The African Medical and Research Foundation (AMREF) is an organization
headquartered

in Nairobi


Kenya
with operations
in seven

African countries

i.e
.

Kenya
, Uganda, Ethiopia, Somalia, Tanzania, South Sudan, and South Africa.
AMREF is facilitating a community participatory approach to knowledge
Management in the health sector. AMREF has
partnered with local communities,
health system formulators and governments with an aim of
realizing

right to health
for all

(Ireri & Wairagu, 2007)
.


(Kora, 2006)

e
valuates the viability of Informatio
n and Communication
Technologies (ICT)
as a KM strategy

in rural
development in Tanzania. None of
the
KM research

initiatives

in East Africa has
formally
focused on Software
6


development, despite being a prominent contributor in the
region’s emerging

knowl
edge
economy.



7


3.

Knowledge Ma
nagement Models



(Kakabadse et al., 2003)

while extending

the work of
(Swan & Newell, 2000)

provide
d

for
F
ive

useful

mode
ls
of KM , where each model treat KM
initiatives

differently
. The
y identified
the models

as follows:

a)

Philosophy based model


it’s

concerned with the epistemology of
knowledge or what constitutes knowledge, the relationship of the
constituents and other n
otions such as truth, justification, causation, doubt
and revocability.
The model provides a high level perspective that requires
reflections in areas of practice. It’s mainly grounded on Socratic view of
knowledge as justified true belief and wisdom as h
ighest constituent in the
knowledge continuum.
Proponents of
this model

argue

that KM needs not be
technology centred.

b)

Cognitive Model

:

this model is rooted on recognition of knowledge as an
economic asset. It focuses on organisational perspective

of kno
wledge
and
considers ICT as an enabler of the knowledge management process.
(Swan
& Newell, 2000)
,
(Zack, 1999)

questions

the application of
this model

and

its variant
s such as SECI Model

(Socialization, Externalization,
Combination, Internalization) proposed by
(Nonaka & Konno, 1998)

in
rapidly changing environment

characterized

by

technology discontinuity



such as software development.



c)

Network model is

b
ased on socialization of knowledge and relationships

of
actors;

the model highlights the role of social patterns

between individuals

and interest groups
in knowledge creation,
sharing and
transfer.
It has the
advantage of
focusing

on external sources of knowledge through
interest and
practice

networks.

It

inoculates
the collaborative

aspect

of creating
knowledge and sharing which
is

a key factor in software
development

espec
ially in geographically dispersed
teams

(Hemetsberger & Reinhardt,
2003)
.


d)

Community of
practice (CoP)

model

-
:


the term CoP was coined by
Jean
L
av
e

and
Etienne Wenger
, who described it as “groups of people informally
bound together by shared expertise and passion for a joint
8


enterpris
e”
(Wenger & Snyder, 2000)

.
The work of

(Sharratt & Usoro, 2003)

d
ifferentiated CoP from usual work teams

and organizational

functional
units in that they are self

organizing

systems and their existence is guided
by membership perceived gains. These communities are not constrained by
time and space and can span beyond
organizational

boundaries.



(Swan & Newel
l, 2000)

contend that trust based rules of engagement are a
critical factor to the success of this model. This model provides a good
background for KM initiatives in software engineering especially open
source
development.


e)

Quantum Model is

based on rec
ent advances in quantum
computing , the
assumes that application of quantum computing to the constituents of
knowledge will lead to high level complexity and improved rationality in
decision making as actors in given scenarios in a the context of appli
c
ation.

This model is not appropriate for use in low resourced
communities.


3.1
Knowledge Management in Software Development


There are

two scenarios on human generated uncertainty in the software
development

(Dekhtyar, Hayes, & Goldsmith, 2007)
.

They include:

a)

Uncertainty

on th
e process which includes issues like : How long will it
take?, what is the most efficient development methodology
?, the choice of
language and environment

b)


Uncertainty on the
product which

includes issues
like:

How much of
security features is required (th
e tradeoff between usability and
security)
.



There is increasing use of Global Software Development (GSD) teams inform of
globally distributed subsidiaries of the same organization, outsourced comp
anies,
open source communities or

collaborating virtu
al companies

which are

distributed globally working on complex software projects

(Avram, 2007;
Hemetsberger & Reinhardt, 2003)
.





9


Integration of KM in the Software development environment context can be used
to improve on the quality of the product (
process output), the process quality itself
and reduce on uncertainty associated with software development. Software
development regardless of the nature and the level of uncertainty is collaborative
and requires intensive human decision

especially
when
ad
aptive

development
methodologies

are used

(Dekhtyar et al., 2007)
.

Throughout the software
development lifecycle, collaboration is among actors with differing expertise.


We formulate three
scenarios

that
illustrate

the different levels
of differing
expertise

that result from
l
evel of experience

gained through
practice
and
/
or
Interest
:


Scenario 1
:


Where the

customer is
a research

hospital in need of
Hospital

Management software,

the
customer may

understand his domain
well i.e
.
m
edicine

and
h
ospital management but may have zer
o knowledge on software
domain.
The customer may even have the knowledge on the application of
softwares in his domain but not the development of softwares.


Scenario
2
:

In an open source community developing
antivirus

software may
have medical

doctors
wi
th interest

(Community of
Interest)

in the
study of
computer viruses and other malicious
software,

they may have Zero knowledge on
the software development

but their domain expertise

on
viruses is

necessary for the
success of the
project.


Scenario 3
: In
a Global Software Development (GSD) all stages in the software
development lifecycle are carried in culturally diverse environment
s,

the software
engineers may have differing experiences in the practice of software engineering.


3.2
The Hybrid Framework
for
KM in

Software Engineering


To cater for these unique circumstances
as illustrated through the
three
scenarios
given
above
, for example,
the presence
of

both

communities of inter
est and
communities of practice.
W
e

therefore,
propose
a hybrid
framework
for

KM
10


process that

blends
philosophical
, cognitive
, CoP and network models
and

can
be
effective in software

development

environment.

11


The Figure 2

below shows
the proposed

hybrid

framework.

The

stages of

knowledge management

in software development

wit
hin the

framework are
:

a)

Knowledge Creation

b)

Validation and Audit

c)

Transfer

d)

Consolidation of best practices

e)

Documentation


In the next sub
-
sections,

w
e discuss

the granules that make up each of the stages
and highlight
its
building

blocks of achieving a co
mprehensive KM process in the
software development
and its probable implementation
in East

Africa.


a)

Knowledge Creation


Knowledge creation is the
most important area of focus within knowledge
management

since this

stage inputs have

far reaching effects

on the preceding
stages of the KM
framework
(Wickramasinghe, 2006)
.


12


Knowledge can be creat
ed by people and/or technologies or be embedded in
processes

as shown in the
Figure 3:
KM Triad

adapted from

(Wickramasinghe,
2006)
.







In
GSD,

knowledge creation involves virtual teams each made up of the three
aspects people, technologies and processes

who can either be localized or
distibuted
.


The knowledge creation process in group envir
onment has be dealt
with in depth by
(Drach
-
Zahavy & Somen, 2001; Gibson, 2001; Mitchell &
Nicholas, 2004, 2006)

(Mitchell & Nicholas, 2006)
.The
ir

studies identifies four
group knowledge creating processes .


The first process is
accumulation of

knowledge on individuals originating from
their functional areas or
practice.
The
authors identify only
the com
munities of
practice
as the probable affiliation of these individuals. In software development,
the communities of interest have significa
nt role in building
shared knowledge

base. These individuals who have interest in the software environment are not
bou
nded by practice
but context

of interest.


In
our proposed

framework both
communities
are
considered.



The second Process is Interaction; this involves team sharing the accumulated
individual
istic
knowledge

to create new individual and team knowledge. Sin
ce
Knowledge is context dependant, members of localized or virtual teams are
influenced by their interest to participate in this process.
In software development
and virtual teams this process involves collaborative technologies such as
groupware,


13


Analysi
s is the third
process of group

knowledge
creation; in

this process group
members explore their
experiences in

comparison with other team members.
Group analogical reasoning in software engineering have numerous applications

across the development lif
ecycle

such as requirements engineering where
customers view the system largely from the usability perspective (usability
requirements) are able to share with developers
.


The fourth process is integration and
creation which

encapsulates consensus

building

on the experiences and analogies.

It can be facilitated
through story

telling.


To sustain the group knowledge creation

and the preceding stages of the hybrid
framework,

two major
continuous activities

are


initiated
:

(a)

Laying f
oundation

for
building knowledge
taxonomies
.


(Whittaker &
Breininger, 2008)

provides a detailed approach on building taxonomies
.




(b)

C
ommunity
S
ocial
N
etwork
A
nalysis

(SNA
)

for measuring centrality

and expert

localization
. The study by

(Dekker & Hendriks, 2006)

lay much
emphasizes on Social Network Analysis
.


b)

Knowledge
Validation and

Audit

Various
role
s and

tools are
emergent in

the process

of
building taxonomies and
expert
localization.

Us
ing

an organizational perspective of KM
, there are
various
roles and tools that may emerge in the organization to carry out key steps in KM
validation and audit (Mod
ification, translation and Repurposing
(Rao, 2005)
.


(See
Figure
4:

Madanmohan Rao
on
Roles

online Communities
)
ad
a
pted from


14



There are other authors who
discuss an organizational and a community view of
knowledge management

(Bourhis, Dubé
, & Jacob, 2005; Fontaine, 2001)
.

The
y

illustrate


various

roles in knowledge validation and audit
,

highlighting the role of
leadership as a critical pillar in
Online Community
Knowledge Validation and
audit

see
Figure 5;

Typology of community roles

.
adapted from
(Bourhis et al.,
2005; Fontaine, 2001)

15



Techniques such as
members’

contribution valuation through
community
assessment
, rating/ ranking/
scoring can be

used. This is a common practice in
existing online user
communities.


c)

Knowledge Transfer

Effective transfer of Knowledge between knowledge workers is one of the key
challenges in KM

(Alavi & Leidner, 2001; Joshi, Saonee.Sarker, & Sarker, 2004)

.

(Jacobson, 2006)

defines knowledge transfer as
:



An exchange of knowledge in which the focus is on structural capital
(knowledge that has been built into processes, products, or services) and on
the transformation of individual knowledge to gro
up knowledge or
organizational knowledge”


16



In tandem with our proposed framework ,
(Jac
obson, 2006)

definition reinforces
the view that
there

must exist a normative structure that provides for

knowledge
flow in the intended community.
(King, 2006)

g
ives an organizational view in the
transfer process and argues that knowledge transfer is effective
if the

sender and
receiver are in
homogeneous contexts.

Self motivation

of knowledge workers,
awareness and
acceptance of the
transfer goals

are some
of factors

positively
influence knowledge transfer.


d)

Documentation
and consolidation of best
practices.

These stages cater for

the future use of assimilated
knowledge.
In software
development,

knowledge reuse can
be seen as an extension
of components

re
-
use
that

reduce on new software development costs. Lesson
learnt systems

can be
used
to catalogue

the experiences

gained through all the stages of knowledge
management

for knowledge reuse
.

Knowledge calibration i
s the correspondence between accuracy o
f knowledge
and confidence of knowledge as a reliable base where knowledge workers can
confidently base their decision
(Goldsmith & Pillai, 2006)
.
A detailed literature

on

knowledge calibration
is available in
(Goldsmith & Pillai, 2006)
.


Conclusions

and Areas

of Further Research


The paper begins with an introduction
to KM and a brief r
eview
of
KM initiatives
.
Then an

exploratory
literature
study on

existence
of KM initiative is carried
out on

software development in East Africa

which focuses beyond the organizational
view.
The
study establishes

there is no formal study

or open initiativ
e
for KM

in

software
development
in

the
region. Based on these
findings,

we

explore
d
the
existing

major
models

of KM on
their viability and

application in software
development.
Following an assessment of individual
models, we propose a
generic
framework
that blends the four major models
. The proposed framework is
intended
to be
the
starting point for

KM initiative among stakeholders in the software
industry in East Africa
.

The
authors discuss

the various stages of
this framework

and their

output.
We confe
r that the


u
se of this
framework can

help the software
17


developers in East
Africa create
, use and share
valuable

experience
s that will give
them
competitive

advantage

in the global market
.

What remains to be seen is how
this framework can be incorporated
to formalize the KM initiative among the
software development community. We suggest
research be undertaken for
determining:

a)

Comparative effectiveness of the proposed framework in

the development

of
local
mobile
content
.

b)


The
benchmarking
standards

for
best

practices
. An empirical study can be
conducted.

c)

The viability
of developing
and using open source technologies that extends
beyond cultural barriers such as
use of

natural
language
processing

facilities
at

various stages of this framework.


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20


Brief Bio
-
data about the authors
:

Karanja Evanson holds an Msc in computer science (Makerere University)
with a bias in
machine learning and security, among other qualifications.

He is an ICT enthusiast, a
Lecturer

in ICT

and an entrepreneur. His research interests includes, programming
languages
, E
-

government, knowledge management and Information security
.


Lawrence Xavier Thuku : Thuku has Master of Science in Information Technology
(MSc. IT) from Strathmore University and various ICT industry Certifications . He is
Lecturer at the Institute of advanced Technology and a consultant . His resear
ch interests
includes Workflow Management systems , Decision Support systems and knowledge
management.


John P. Kangethe Karanja

holds a MBA (Finance) from University of Nairobi , CPA and
CPS among other qualifications. He has taught Finance and acco
unting courses at various
Universities in Kenya.